/*
** ScoredFeatures.java
**
** Initial Version - 07/01/2017
** Author - Adam Pocock
** 
** Part of the FEAture Selection Toolbox (FEAST), please reference
** "Conditional Likelihood Maximisation: A Unifying Framework for Information
** Theoretic Feature Selection"
** G. Brown, A. Pocock, M.-J. Zhao, M. Lujan
** Journal of Machine Learning Research (JMLR), 2012
**
** Please check www.github.com/Craigacp/FEAST for updates.
** 
** Copyright (c) 2010-2017, A. Pocock, G. Brown, The University of Manchester
** All rights reserved.
** 
** Redistribution and use in source and binary forms, with or without modification,
** are permitted provided that the following conditions are met:
** 
**   - Redistributions of source code must retain the above copyright notice, this 
**     list of conditions and the following disclaimer.
**   - Redistributions in binary form must reproduce the above copyright notice, 
**     this list of conditions and the following disclaimer in the documentation 
**     and/or other materials provided with the distribution.
**   - Neither the name of The University of Manchester nor the names of its 
**     contributors may be used to endorse or promote products derived from this 
**     software without specific prior written permission.
** 
** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
** ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED 
** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 
** DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
** ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES 
** (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 
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** (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 
** SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
**
*/
package craigacp.feast;

/**
 * A tuple class which contains the selected feature indices and their scores.
 *
 * @author craigacp
 */
public class ScoredFeatures {

    public final int[] featureIndices;
    public final double[] featureScores;

    public ScoredFeatures(int[] featureIndices, double[] featureScores) {
        this.featureIndices = featureIndices;
        this.featureScores = featureScores;
    }

}
